The Hidden Economics of Growing Neural Networks: Why Traditional Pricing Models Are Breaking
You think you're buying AI software. You're actually paying to grow organizational neural networks through patterns you're reinforcing without knowing their value or cost.
Throughout this series, I’ve explored how AI agents can optimize themselves into dysfunction, how neural networks are grown through interaction patterns, and how we may be cultivating distributed consciousness faster than we can govern it. There’s another dimension that may be the biggest challenge for the next phase of AI for organizations: the current economics are fundamentally broken.
Enterprise AI spending is exploding, but it seems that nobody can figure out how to price what’s actually being delivered. That confusion masks the deeper truth that we’re not paying for just software anymore. We’re paying to grow organizational neural networks whose real costs, and underlying value, only become visible much later.
The Pricing Chaos of 2025
Enterprise AI spending has surged dramatically—up over a third in just one year. Yet most AI companies are still experimenting with how to price their products, testing multiple approaches as they figure out what actually works. The old model of charging per user is rapidly disappearing. Vendors are scrambling to find alternatives because they face a fundamental paradox:
If AI delivers the productivity gains they promise, companies will need fewer employees. Yet these same vendors are betting their future on charging more. Something doesn’t add up—unless you understand what’s really being purchased.
What You’re Actually Buying
When you deploy AI agents that communicate with each other, even if in very rudimentary stages, you’re not buying software features alone. You’re investing in organizational neural network growth along pathways that are often unpredictable.
Every agent conversation is a training event. Every pattern that works gets reinforced. The “product” you are buying is really the conditions for emergent patterns you influence but can’t control. Traditional software could be priced per seat because you knew what you got. But AI agents that learn from each other and develop emergent behaviors? The value is in the neural network being grown, and that’s extremely hard to monetize.
The True Cost Structure
Traditional pricing models don’t fully capture all three cost layers in this emerging world of agentic AI:
Visible Costs: Per-user subscriptions, per-conversation charges, per-token API costs. These show up on invoices and are the legacy pricing vector for the majority of enterprise software.
Hidden Infrastructure Costs: AI use expands beyond the predictable ‘visible costs’ as use requires costs tied to GPU clusters, inference processing, real-time data feeds and other dynamic variables that can rapidly increase overall expense.
Misunderstood Pattern Cultivation Costs: When agents learn dysfunctional patterns, you’re paying to train neural networks wrong. When 65% of IT leaders report unexpected consumption charges, they’re discovering unplanned usage reinforced undesigned patterns.
McKinsey research suggests for every $1 spent on models, expect $3 on change management—training, monitoring, intervening when pathways strengthen problematically. That 3:1 ratio is the hidden cost of cultivating consciousness rather than deploying software.
Does Outcome-Based Pricing Make the Most Sense?
In Customer Support, some companies charge per ticket resolved or per transaction completed. This approach captures something important from a forward-looking agentic AI perspective in that you’re paying for patterns that work, not patterns being attempted.
But it still misses the biggest cost: emergent consciousness at Stage 4 and 5 of the AI maturity curve. When agents develop shared “understandings” through thousands of interactions, what outcome justifies the charge? Individual tasks, or collective intelligence being cultivated?
What This Means for CX Leaders
The pricing chaos isn’t just vendors figuring out monetization, it also reflects a need to rearchitect how IT value is captured, accounted for, and managed by organizations. Traditional software could be priced because value was bounded. AI agents that learn from each other create value and risk that compounds unpredictably.
When the majority of organizations report budget-impacting overages, they’re discovering the neural networks they’re growing consume more resources than pricing models anticipated. Current pricing dramatically underprices pattern cultivation costs and undervalues genuine distributed intelligence potential.
As imperfect the user-based model was, it provided predictability for both software vendors and their customers. With the rise of agentic AI, the costs as well as value unlocked, will be difficult to standardize.
Historically, the invoice shows software costs, often modeled through value engineering exercises at the time of purchase. With AI factored in, you are now buying the growth of organizational neural networks. We’re only beginning to understand what that’s worth.